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New method for prediction and solving the problem of drilling fluid loss using modular neural network and particle swarm optimization algorithm

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2014年第4期4卷 371-379页

作者： Toreifi, Hojjat Rostami, Habib Manshad, Abbas Khaksar Persian Gulf Univ Sch Engn Oil Engn Dept Bushehr Iran Persian Gulf Univ Sch Engn Comp Engn Dept Bushehr Iran Petr Univ Technol Abadan Fac Petr Engn Dept Petr Engn Abadan Iran

Loss circulation is a common problem in drilling industry that causes high expenditure on drilling companies. Nowadays minimizing of loss circulation is a main goal and preference for drilling engineers. Artificial intelligence (Al) is a new method of solving engineering problems that has the ability to consider all effective parameters simultaneously. Moreover, it has generalization and the ability to learn directly from field data. In this paper, two models were designed using Al and data of 38 wells located in Maroun oil field. Both models were developed by modular neural network, to predict loss circulation in quality and quantity. Then, the particle swarm optimization algorithm was used to minimize loss circulation. The accuracy of two models in predicting loss circulation quantitatively and qualitatively is 0.94 and 0.98 %, respectively.

关键词： Loss circulation Modular neural network Loss circulation reduction particle swarm optimization algorithm

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An adaptive particle swarm optimization algorithm for reservoir operation optimization

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APPLIED SOFT COMPUTING 2014年第0期18卷 167-177页

作者： Zhang, Zhongbo Jiang, Yunzhong Zhang, Shuanghu Geng, Simin Wang, Hao Sang, Guoqing Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China

Reservoir operation optimization (ROO) is a complicated dynamically constrained nonlinear problem that is important in the context of reservoir system operation. In this study, improved adaptive particle swarm optimization (IAPSO) is proposed to solve the problem, which involves many conflicting objectives and constraints. The proposed algorithm takes particle swarm optimization (PSO) as the main evolution method. To overcome the premature convergence of PSO, adjusting dynamically the two sensitive parameters of PSO guides the evolution direction of each particle in the evolution process. In the IAPSO method, an adaptive dynamic parameter control mechanism is applied to determine parameter settings. Moreover, a new strategy is proposed to handle the reservoir output constraint of ROO problem. Finally, the feasibility and effectiveness of the proposed IAPSO algorithm are validated by the Three Gorges Project (TGP) with 42.23 bkW power generation and XiLuoDo Project (XLDP) with 30.10 bkW. Compared with other methods, the IAPSO provides a better operational result with greater effectiveness and robustness, and appears to be better in terms of power generation benefit and convergence performance. Meanwhile, the optimal results could meet output constraint at each interval. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Reservoir operation optimization Adaptive particle swarm optimization algorithm Constraints

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A particle swarm algorithm optimization-based SVM-KNN algorithm for epileptic EEG recognition

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第12期37卷 11233-11249页

作者： Wang, Xiaoying Ling, Yu Ling, Xiang Li, Xianghuan Li, Zhicheng Hu, Kunpeng Dai, Min Zhu, Jia Du, Yuxiao Yang, Qintai Sun Yat Sen Univ Affiliated Hosp 3 Informat Ctr Guangzhou 510630 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510630 Peoples R China Sun Yat Sen Univ Affiliated Hosp 3 Dept Otolaryngol Head & Neck Surg Guangzhou Peoples R China Sun Yat Sen Univ Affiliated Hosp 3 Dept Gen Surg Guangzhou Peoples R China Sun Yat Sen Univ Affiliated Hosp 3 Dept Tradit Chinese Med Guangzhou Peoples R China Zhejiang Normal Univ Key Lab Intelligent Educ Technol & Applicat Zheji Jinhua Zhejiang Peoples R China

Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K-nearest neighbor optimized by particle swarm algorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto-detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (>= 95%) with only a small number of training samples, which has a good clinical application value.

关键词： empirical modal decomposition epilepsy EEG recognition K-nearest neighbor particle swarm optimization algorithm support vector machine

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Inverse Estimation of Surface Radiation Properties Using Repulsive particle swarm optimization algorithm

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TRANSACTIONS OF THE KOREAN SOCIETY OF MECHANICAL ENGINEERS B 2014年第9期38卷 747-755页

作者： Lee, Kyun Ho Kim, Ki Wan Sejong Univ Sch Mech & Aerosp Engn Dept Aerosp Engn Seoul South Korea Agcy Def Dev Daejeon South Korea

The heat transfer mechanism for radiation is directly related to the emission of photons and electromagnetic waves. Depending on the participation of the medium, the radiation can be classified into two forms: surface and gas radiation. In the present study, unknown radiation properties were estimated using an inverse boundary analysis of surface radiation in an axisymmetric cylindrical enclosure. For efficiency, a repulsive particle swarm optimization (RPSO) algorithm, which is a relatively recent heuristic search method, was used as inverse solver. By comparing the convergence rates and accuracies with the results of a genetic algorithm (GA), the performances of the proposed RPSO algorithm as an inverse solver was verified when applied to the inverse analysis of the surface radiation problem.

关键词： Surface Radiation Inverse Estimation particle swarm optimization algorithm Genetic algorithm

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Hybrid particle swarm optimization with Bat algorithm 8

Hybrid Particle Swarm Optimization with Bat Algorithm

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8th International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Pan, Tien-Szu Dao, Thi-Kien Trong-The Nguyen Chu, Shu-Chuan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 807 Taiwan Haiphong Private Univ Fac Informat Technol Le Chan Hai Phong Vietnam Flinders Univ S Australia Sch Comp Sci Engn & Math Adelaide SA Australia

ISBN: (纸本)9783319122854

In this paper, a communication strategy for hybrid particle swarm optimization (PSO) with Bat algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.

关键词： Hybrid particle swarm optimization with Bat algorithm particle swarm optimization algorithm Bat algorithm optimizations swarm Intelligence

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Research on classification of privacy protection based on Improved particle swarm optimization algorithm

Research on classification of privacy protection based on Im...

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4th International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2013)

作者： Chen Yu Tang Yuanxin Zhou Zhou Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

ISBN: (纸本)9783037859926

In this paper, through the analysis of the characteristics of particle swarm optimization algorithm, combined with the specific circumstances of Bayesian network structure learning, proposed to based on improved particle swarm algorithm. The algorithm uses the BIC measure function as a standard Bayesian network, while preserving the optimal particle case, the possibility of a mutation operation is added to decrease the algorithm into a local optimum. Through a typical Asia network, show that the algorithm is feasible, and other related algorithm is better than the experiment, the effectiveness of the algorithm. In this paper, the algorithm is verified from two aspects of theory and experiments, the results show that the algorithm is feasible.

关键词： Privacy protection Bayesian networks data mining particle swarm optimization algorithm

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A Improved particle swarm optimization algorithm for the Application with Boundary-layer Problems of Power-law Fluids

A Improved Particle Swarm Optimization Algorithm for the App...

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International Conference on Mechanical Engineering and Instrumentation (ICMEI)

作者： Zhou, Jianhui Yue, Lixi Lu, Yannan Hebei United Univ Coll Qianan Tangshan 064400 Peoples R China

ISBN: (纸本)9783038350095

In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suction/injection as well as a reverse flow boundary conditions. A improved particle swarm optimization algorithm (ISPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.

关键词： particle swarm optimization algorithm Ordinary differential equations Parameter estimation boundary-layer problems

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Band selection for hyperspectral image classification based on improved particle swarm optimization algorithm

Band selection for hyperspectral image classification based ...

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4th International Conference on Advances in Materials and Manufacturing (ICAMMP 2013)

作者： Li, Chenming Wang, Yan Gao, Hongmin Zhang, Lili Hohai Univ Coll Comp & Informat Engn Nanjing 211100 Jiangsu Peoples R China

ISBN: (纸本)9783038350156

Hyperspectral images have been widely used in earth observation. However, there are some problems such as huge amount of data and high correlation between bands. An application of particle swarm optimization algorithm based on B distance was proposed to band selection of hyperspectral images. First of all, bands are grouping by the correlation coefficient of the band and adjacent bands. B distance was used as separability criterion between classes and the fitness function comes into being. Finally, the classification results illustrate that the total classification accuracy of the proposed method is higher than the traditional method.

关键词： hyperspectral image B distance particle swarm optimization algorithm band selection

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Parameter optimization of ADRC for spacecraft attitude maneuver based on particle swarm optimization algorithm 6

Parameter optimization of ADRC for spacecraft attitude maneu...

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6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Wang, Ping Wang, Hua Bai, Guoyu Su, Lin Co Postgrad Management Acad Equipment Beijing Peoples R China Acad Equipment Dept Space Equipment Beijing Peoples R China Natl Def Intellectual Property Bur Beijing Peoples R China

ISBN: (纸本)9781479949557

In this paper, parameter of ADRC for spacecraft attitude maneuvering is optimizated. Nonlinear dynamics model of spacecraft attitude describes attitude motion. particle swarm optimization algorithm is used for parameter optimization of ADRC. The controller index which describes attitude adjustment capacity of three axes is designed. The influence of controller parameter is quantifiable on the control performance. The selection of parameter based on traditonal experience is avoided. Simulation results show that: the particle swarm optimization algorithm for system updates through the position and velocity. The system can quickly converge to the global optimal solution, and the parameter of ADRC is optimized.

关键词： ADRC particle swarm optimization algorithm Parameter optimization

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The Application of particle swarm optimization algorithm on Absorbent Materials

The Application of Particle Swarm Optimization Algorithm on ...

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2nd International Conference on Mechanics and Control Engineering (ICMCE 2013)

作者： Feng, Hongquan Wu, Bingheng Liu, Yuanyun Liao, Yi Gu, Hao Yu, Xing Shanghai Radio Equipment Res Inst 203 Liping RD Shanghai 200090 Peoples R China Shanghai Key Lab Elect Environm Effects Aerosp Ve Shanghai 200438 Peoples R China

ISBN: (纸本)9783037859087

The thickness of multi-layer absorbing material is optimized to obtain lower electromagnetic reflection coefficient by using particle swarm optimization (PSO) algorithm in this paper. Two examples are employed to validate the excellent performance of PSO. The results show that the reflection coefficient of absorbing material is less than-20 dB over the bandwidth of 2GHz similar to 18GHz, less than -25 dB over the narrowband of 9 GHz similar to 11GHz, less than-30 dB during the bandwidth of 9.5 GHz similar to 10.5 GHz. It also shows that the minimum value approaches to -48 dB in a certain range.

关键词： particle swarm optimization algorithm Absorbing material optimization design

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